Adapting Regenerative Braking Strength to Driver Preference
Bibliographic record
Abstract
The modern automotive industry has witnessed a growing emphasis on adapting the driving experience to individual drivers. With the rising popularity of electrified vehicles, the implementation of regenerative braking systems, specifically lift-off regenerative braking, has become a focal point. However, research indicates that drivers often find the predefined deceleration response during lift-off regenerative braking to be undesirable. This thesis addresses this issue by developing an adaptive regenerative braking controller that learns driver preferences, thereby fulfilling the objective of enhancing the driving experience of lift-off regenerative braking systems by reducing driver fatigue through the minimization of pedal interventions. The research focuses on three critical aspects: accurate identification of driving conditions, acquisition of driver preferences for lift-off regenerative braking, and compatibility with real-time automotive hardware. By leveraging advanced techniques like HDBSCAN clustering, fuzzy logic inference, and online Q-learning, the research achieves accurate driving condition identification and adaptation to individual driver preferences in a control scheme that can be practically deployed in-vehicle. Real-world testing demonstrates the controller's 80.9 % accuracy in identifying driving conditions as well as its successful learning of the driver's preferred deceleration to within 1.9 %. Subsequently, the adaptive regenerative braking controller results in a 23.2 % reduction in pedal interventions during deceleration compared to a baseline that is representative of an industry-standard implementation of lift-off regenerative braking. This outcome underscores the controller's potential to alleviate driver fatigue and enhance the overall driving experience. This research contributes to the advancement of electrified vehicle powertrain control, focusing on improving driver acceptance and satisfaction with regenerative braking systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".